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Forecasting Time Series Data with Facebook Prophet

You're reading from   Forecasting Time Series Data with Facebook Prophet Build, improve, and optimize time series forecasting models using the advanced forecasting tool

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800568532
Length 270 pages
Edition 1st Edition
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Author (1):
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Greg Rafferty Greg Rafferty
Author Profile Icon Greg Rafferty
Greg Rafferty
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Getting Started
2. Chapter 1: The History and Development of Time Series Forecasting FREE CHAPTER 3. Chapter 2: Getting Started with Facebook Prophet 4. Section 2: Seasonality, Tuning, and Advanced Features
5. Chapter 3: Non-Daily Data 6. Chapter 4: Seasonality 7. Chapter 5: Holidays 8. Chapter 6: Growth Modes 9. Chapter 7: Trend Changepoints 10. Chapter 8: Additional Regressors 11. Chapter 9: Outliers and Special Events 12. Chapter 10: Uncertainty Intervals 13. Section 3: Diagnostics and Evaluation
14. Chapter 11: Cross-Validation 15. Chapter 12: Performance Metrics 16. Chapter 13: Productionalizing Prophet 17. Other Books You May Enjoy

Chapter 3: Non-Daily Data

When Prophet was first released, it assumed all data would be on a daily scale, with one row of data per day. It has since grown to handle many different granularities of data, but because of its historical conventions, there are few things to be cautious of when working with non-daily data.

In this chapter, you will look at monthly data (and in fact, any data that is measured in timeframes greater than a day) and see how to change the frequency of predictions to avoid unexpected results. You will also look at hourly data and observe an additional component in the components plot. Finally, you will learn how to handle data that has regular gaps along the time axis.

This chapter will cover the following:

  • Using monthly data
  • Using sub-daily data
  • Using data with regular gaps
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